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CHAPTER 29

Analytics

Thanks in part to vigorous efforts by vendors (led by IBM) to bring the idea to a wider public, analytics is coming closer to the mainstream. Whether in ESPN ads for fantasy football, or election-night slicing and dicing of vote and poll data, or the ever-broadening influence of quantitative models for stock trading and portfolio development, numbers-driven decisions are no longer the exclusive province of people with hard-core quantitative skills backed by expensive, often proprietary infrastructure.

Not surprisingly, the definition of “analytics” is completely problematic. At the simple end of the spectrum, one Australian firm asserts that “[a]nalytics is basically using existing business data or statistics to make informed decisions.”1 Confronting market confusion, Gartner market researchers settled on a similarly generic and less elegant assertion: “Analytics leverage data in a particular functional process (or application) to enable context-specific insight that is actionable.”2

To avoid terminological conflict, let us merely assert that analytics uses statistical and other methods of processing to tease out business insights and decision cues from masses of data. In order to see the reach of these concepts and methods, consider a few examples drawn at random:

The “flash crash” of May 2010 focused attention on the many forms and roles of algorithmic trading of equities. While firm numbers on the practice are difficult to find, it is telling that the ...

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